Information Technology Reference
In-Depth Information
Table 4.4
Settings used in the 10-dimensional data mining problem.
Number of runs
100
Number of generations
1000
Population size
50
Number of fitness cases
100
Function set
+ - * /
Terminal set
a b c d e f g h i j
Head length
6
Gene length
13
Number of genes
5
Linking function
+
Chromosome length
65
Mutation rate
0.044
Inversion rate
0.1
IS transposition rate
0.1
RIS transposition rate
0.1
One-point recombination rate
0.3
Two-point recombination rate
0.3
Gene recombination rate
0.3
Gene transposition rate
0.1
Fitness function
Equation (3.3b)
Selection range
100%
Precision
0.01%
Success rate
77%
where
a
represents the meaningful variable and
b
-
j
represent the remaining
meaningless variables. As its expression shows, this chromosome encodes a
function equivalent to the target function (4.1) (the contribution of each gene
is shown in brackets):
y
(
a
3
)
(
a
h
)
(
(
h
)
(
a
2
)
(4.8b)
4.2 Classification Problems
In this section we will use the basic gene expression algorithm to model three
complex real-world problems, namely, the diagnosis of breast cancer, the as-
signment of a credit card, and the classification of Fisher's irises. The first
